3. Continuing our "advertisement" example as discussed in the class. An ad in the "Machine Learning Daily" for a Learning Machine advertises for a machine with the following properties Property 1: Can learn k monotone disjunctions (disjunctions with at most k attributes) from data, where each example is defined using d binary attributes Property 2: Finds a hypothesis which has zero training error. Property 3: 9 times out of 10, learns a hypothesis which will not make more than 5 mistakes in a test data set of 100 randomly picked examples With these properties, the advertisement makes the following claims about the algorithm: . Claim 1: For a 10 dimensional data set, the learner needs less than . Claim 2: For a 10 dimensional data set, the learner needs less than . Claim 3: For a 20 dimensional data set, the learner needs less than . Claim 4: For a 10 dimensional data set, the learner will need less 100 training examples to learn a 5 monotone disjunction. 176 training examples to learn a 10 monotone disjunction. 300 training examples to learm a 20 monotone disjunction. than 180 training examples to learn a 5 monotone disjunction. Which of the following will be true? O a) Both claims 1 and 2 are correct O b) Claim 3 is incorrect O c) Claim 3 is correct O d) Claim 1 is correct and Claim 2 is incorrec